EHR-DS-QA: A Synthetic QA Dataset Derived from Medical Discharge Summaries for Enhanced Medical Information Retrieval Systems
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This dataset was designed and created to enable advancements in healthcare- focused large language models, particularly in the context of retrieval- augmented clinical question-answering capabilities. Developed using a self- constructed pipeline based on the 13-billion parameter Meta Llama 2 model, this dataset encompasses 21466 medical discharge summaries extracted from the MIMIC-IV-Note dataset, with 156599 synthetically generated question-and-answer pairs, a subset of which was verified for accuracy by a physician. These pairs were generated by providing the model with a discharge summary and instructing it to generate question-and-answer pairs based on the contextual information present in the summaries. This work aims to generate data in support of the development of compact large language models capable of efficiently extracting information from medical notes and discharge summaries, thus enabling potential improvements for real-time decision-making processes in clinical settings. Additionally, accompanying the dataset is code facilitating question-and-answer pair generation from any medical and non-medical text. Despite the robustness of the presented dataset, it has certain limitations. The generation process was confined to a maximum context length of 6000 input tokens, owing to hardware constraints. The large language model's nature in generating these question-and-answer pairs may introduce an underlying bias or a lack in diversity and complexity. Future iterations should focus on rectifying these issues, possibly through diversified training and expanded verification procedures as well as the employment of more powerful large language models.
本数据集专为推动医疗领域大语言模型（Large Language Model）的技术进阶而设计构建，尤其聚焦于检索增强型临床问答能力的研发场景。 本数据集基于参数量达130亿的Meta Llama 2模型，通过自研流水线搭建而成，涵盖从MIMIC-IV-Note数据集中提取的21466份医疗出院小结，以及156599条人工合成的问答对，其中部分问答对已由执业医师完成准确性核验。 上述问答对的生成方式为，向模型输入单份出院小结，并指令其基于文档内的上下文信息生成对应问答对。 本数据集的构建目标为生成支撑轻量化大语言模型研发的数据，使其能够高效从医疗病历与出院小结中提取信息，进而助力临床场景下实时决策流程的优化。 本数据集同步附带可针对任意医疗与非医疗文本生成问答对的配套代码。 尽管本数据集具备一定的鲁棒性，但仍存在若干局限：其一，受硬件条件限制，生成过程的最大上下文输入长度仅为6000个Token；其二，基于大语言模型生成的问答对可能潜藏偏差，且在多样性与复杂度层面存在不足。 后续迭代工作应致力于修正上述问题，可通过多样化训练、扩充核验流程，以及采用性能更强的大语言模型等路径实现。
